Answer:
where is the graph?
Step-by-step explanation:
I can't see the graph so I can't answer!
Answer:
Time of murder = 10:39 am
Step-by-step explanation:
Let the equation of exponential function representing the final temperature of the body after time 't' is,
f(t) = 
Here, a = Initial temperature
n = Constant for the change in temperature
t = Duration
At 11:30 am temperature of the body was 91.8°F.
91.8 = 
--------(1)
Time to reach the body to the morgue = 12:30 pm
Duration to reach = 12:30 p.m. - 11:30 a.m.
= 1 hour
Therefore, equation will be,
84.4 = 
eⁿ = 
ln(eⁿ) = ln(0.9194)
n = -0.08403
From equation (1),
91.8 = 
![ln[(e)^{0.08403t}]=ln[\frac{98.6}{91.8}]](https://tex.z-dn.net/?f=ln%5B%28e%29%5E%7B0.08403t%7D%5D%3Dln%5B%5Cfrac%7B98.6%7D%7B91.8%7D%5D)
0.08403t = 0.07146
t = 0.85 hours
t ≈ 51 minutes
Therefore, murder was done 51 minutes before the detectives arrival.
Time of murder = 11:30 - 00:51
= 10:90 - 00:51
= 10:39 am
Answer:
720 MPH
Step-by-step explanation:
2700 miles / 3.75 hours = 720 MPH
Answer:
the Peter age be 27
Step-by-step explanation:
Let assume peter be
And, then mary is 2p ÷ 3
So
The equation would be
2p ÷ 3 - 2 = 1 ÷ 2 (p + 5)
4p - 12 = 3p + 15
p = 27
So Peter age be 27
And, the mary age is
= 2 (27) ÷ 3
= 18
Hence, the Peter age be 27
1. Introduction. This paper discusses a special form of positive dependence.
Positive dependence may refer to two random variables that have
a positive covariance, but other definitions of positive dependence have
been proposed as well; see [24] for an overview. Random variables X =
(X1, . . . , Xd) are said to be associated if cov{f(X), g(X)} ≥ 0 for any
two non-decreasing functions f and g for which E|f(X)|, E|g(X)|, and
E|f(X)g(X)| all exist [13]. This notion has important applications in probability
theory and statistical physics; see, for example, [28, 29].
However, association may be difficult to verify in a specific context. The
celebrated FKG theorem, formulated by Fortuin, Kasteleyn, and Ginibre in
[14], introduces an alternative notion and establishes that X are associated if
∗
SF was supported in part by an NSERC Discovery Research Grant, KS by grant
#FA9550-12-1-0392 from the U.S. Air Force Office of Scientific Research (AFOSR) and
the Defense Advanced Research Projects Agency (DARPA), CU by the Austrian Science
Fund (FWF) Y 903-N35, and PZ by the European Union Seventh Framework Programme
PIOF-GA-2011-300975.
MSC 2010 subject classifications: Primary 60E15, 62H99; secondary 15B48
Keywords and phrases: Association, concentration graph, conditional Gaussian distribution,
faithfulness, graphical models, log-linear interactions, Markov property, positive
